gluonts
<img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg"> # GluonTS - Probabilistic Time Series Modeling in Python [](https://pypi.org/project/gluonts/) [](./LICENSE) [](https://ts.gluon.ai/) [](https://ts.gluon.ai/dev/) [](https://pepy.tech/projects/gluonts) **📢 BREAKING NEWS**: We released **Chronos**, a suite of pretrained models for zero-shot time series forecasting. Chronos can generate accurate probabilistic predictions for new time series not seen during training. Check it out [here](https://github.com/amazon-science/chronos-forecasting)! GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on [PyTorch](https://pytorch.org) and [MXNet](https://mxnet.apache.org). ## Installation GluonTS requires Python 3.7 or newer, and the easiest way to install it is via `pip`: ```bash # install with support for torch models pip install "gluonts[torch]" # install with support for mxnet models pip install "gluonts[mxnet]" ``` See the [documentation](https://ts.gluon.ai/stable/getting_started/install.html) for more info on how GluonTS can be installed. ## Simple Example To illustrate how to use GluonTS, we train a DeepAR-model and make predictions using the airpassengers dataset. The dataset consists of a single time series of monthly passenger numbers between 1949 and 1960. We train the model on the first nine years and make predictions for the remaining three years. ```py import pandas as pd import matplotlib.pyplot as plt from gluonts.dataset.pandas import PandasDataset from gluonts.dataset.split import split from gluonts.torch import DeepAREstimator # Load data from a CSV file into a PandasDataset df = pd.read_csv( "https://raw.githubusercontent.com/AileenNielsen/" "TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv", index_col=0, parse_dates=True, ) dataset = PandasDataset(df, target="#Passengers") # Split the data for training and testing training_data, test_gen = split(dataset, offset=-36) test_data = test_gen.generate_instances(prediction_length=12, windows=3) # Train the model and make predictions model = DeepAREstimator( prediction_length=12, freq="M", trainer_kwargs={"max_epochs": 5} ).train(training_data) forecasts = list(model.predict(test_data.input)) # Plot predictions plt.plot(df["1954":], color="black") for forecast in forecasts: forecast.plot() plt.legend(["True values"], loc="upper left", fontsize="xx-large") plt.show() ``` ![[train-test]](https://ts.gluon.ai/static/README/forecasts.png) Note, the forecasts are displayed in terms of a probability distribution and the shaded areas represent the 50% and 90% prediction intervals. ## Contributing If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md). ## Citing If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers, in addition to any model-specific references that are relevant for your work: ```bibtex @article{gluonts_jmlr, author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang}, title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {116}, pages = {1-6}, url = {http://jmlr.org/papers/v21/19-820.html} } ``` ```bibtex @article{gluonts_arxiv, author = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C. and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and Türkmen, A. C. and Wang, Y.}, title = {{GluonTS: Probabilistic Time Series Modeling in Python}}, journal = {arXiv preprint arXiv:1906.05264}, year = {2019} } ``` ## Links ### Documentation * [Documentation (stable)](https://ts.gluon.ai/stable/) * [Documentation (development)](https://ts.gluon.ai/dev/) ### References * [JMLR MLOSS Paper](http://www.jmlr.org/papers/v21/19-820.html) * [ArXiv Paper](https://arxiv.org/abs/1906.05264) * [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluonts/tree/dev/REFERENCES.md): a bibliography. ### Tutorials and Workshops * [Tutorial at IJCAI 2021 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-IJCAI-2021/) with [YouTube link](https://youtu.be/AB3I9pdT46c). * [Tutorial at WWW 2020 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-WWW-2020/) * [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) * [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) * [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) * [Neural Time Series with GluonTS](https://youtu.be/beEJMIt9xJ8) * [International Symposium of Forecasting: Deep Learning for Forecasting workshop](https://lostella.github.io/ISF-2020-Deep-Learning-Workshop/)